Quantifying the calcification of abdominal aorta and major side branches with deep learning

Abstract

AIM: To explore the possibility of a neural network-based method for quantifying calcifications of the abdominal aorta and its branches. MATERIALS AND METHODS: In total, 58 computed tomography (CT) angiography volumes were selected from a dataset of 609 to represent different stages of sclerosis. The ground truth segmentations of the abdominal aorta, coeliac trunk, superior mesenteric artery, renal arteries, common iliac arteries, and their calcifications were delineated manually. Two V-Net ensemble models were trained, one for segmenting arteries of interest and another for calcifications. The branches of interest were shortened algorithmically. The volumes of calcification were then evaluated from the arteries of interest. RESULTS: The results indicate that automatic detection is possible with a high correlation to the ground truth. The scores for the ensemble calcification model were dice score of 0.69 and volumetric similarity (VS) of 0.80 and for the arteries of interest segmentations: aorta: dice 0.96, VS 0.98; aortic branches: dice 0.74, VS 0.87; and common iliac arteries: dice 0.72, VS 0.91. CONCLUSIONS: The presented neural network model is the first to be capable of automatically segmenting, in addition to calcification, both the aorta and its branches from contrast-enhanced CT angiography. This technology shows promise in addressing limitations inherent in earlier methods that relied solely on plain CT.